用文本控制生成时间序列，提升真实性和可控性。
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
- 基于大模型多智能体迭代优化，合成多样文本-时序数据。
- 在12个数据集上11个达顶尖生成质量，可控性提升6%-12%。
- 适合需要精准时序数据生成的金融、医疗等场景。
时间序列生成（TSG）在仿真、数据增强和反事实分析中有广泛应用。现有方法虽在无条件单领域生成上表现良好，但现实应用需跨领域、可控制的生成能力，以满足领域约束与实例级需求。本文提出文本控制的时序生成任务，利用文本描述提供语义、领域信息及具体时间模式，指导生成过程。针对该场景的数据稀缺问题，我们设计基于大语言模型的多智能体框架，自动生成多样化、真实的文本到时序数据集。进一步提出BRIDGE框架，融合语义原型与文本描述，实现领域级引导。该方法在12个数据集中的11个达到最先进生成保真度，在无文本输入对比下，均方误差降低12%，平均绝对误差降低6%，展现出生成定制化时序数据的潜力。
原文摘要 · Abstract (English)
Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain TSG, real-world applications demand for cross-domain approaches capable of controlled generation tailored to domain-specific constraints and instance-level requirements. In this paper, we argue that text can provide semantic insights, domain information and instance-specific temporal patterns, to guide and improve TSG. We introduce ``Text-Controlled TSG'', a task focused on generating realistic time series by incorporating textual descriptions. To address data scarcity in this setting, we propose a novel LLM-based Multi-Agent framework that synthesizes diverse, realistic text-to-TS datasets. Furthermore, we introduce BRIDGE, a hybrid text-controlled TSG framework that integrates semantic prototypes with text description for supporting domain-level guidance. This approach achieves state-of-the-art generation fidelity on 11 of 12 datasets, and improves controllability by up to 12% on MSE and 6% MAE compared to no text input generation, highlighting its potential for generating tailored time-series data.
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